This codebook demonstrates the data ingestion and processing from Washington Department of Health’s Summary Financial Data. This includes:
## [1] "total_current_assets" "total_board_designated_assets"
## [3] "total_property_plant_equipment" "less_accumulated_depreciation"
## [5] "net_property_plant_equipment" "total_investments_other_assets"
## [7] "total_intangible_assets" "total_assets"
## [9] "total_current_liabilities" "total_deferred_credits"
## [11] "total_long_term_debt" "less_current_maturities_of_ltd"
## [13] "net_long_term_debt" "unrestricted_fund_balance"
## [15] "total_equity" "total_liab_fund_bal_or_equity"
## [1] "deductible" "markup"
## [3] "operating_margin" "total_margin"
## [5] "expense_to_revenue" "nonoperating_revenue"
## [7] "reported_income_index" "return_on_total_assets"
## [9] "return_on_equity" "growth_rate_in_equity"
## [11] "current" "liabilities_to_assets"
## [13] "days_in_patient_ar" "average_payment_period"
## [15] "days_cash_on_hand" "equity_financing"
## [17] "long_term_debt_to_equity" "fixed_asset_financing"
## [19] "cash_flow_to_total_debt" "capital_expense"
## [21] "times_interest_earned" "debt_service_coverage"
## [23] "long_term_debt_to_depreciation" "total_asset_turnover"
## [25] "fixed_asset_turnover" "current_asset_turnover"
## [27] "inventory" "average_age_of_plant"
## [29] "financial_viability_index"
## [1] "inpatient_revenue" "outpatient_revenue"
## [3] "total_patient_revenue" "bad_debts"
## [5] "contractual_adjustments" "charity_care"
## [7] "other_adjustments" "total_deductions_from_revenue"
## [9] "net_patient_service_revenue" "other_operating_revenue"
## [11] "tax_revenue" "total_operating_revenue"
## [13] "salaries_and_benefits" "employee_benefits"
## [15] "professional_fees" "supplies"
## [17] "purchased_services_utilities" "purchased_services_other"
## [19] "depreciation" "rentals_and_leases"
## [21] "insurance" "license_and_taxes"
## [23] "interest" "other_direct_expenses"
## [25] "total_operating_expenses" "net_operating_revenue"
## [27] "non_operating_income" "net_before_extraordinary_items"
## [29] "extraordinary_items" "federal_income_tax"
## [31] "net_revenue_or_expense"
## [1] "births" "admissions"
## [3] "patient_days" "acmvus"
## [5] "adj_adm" "adj_pat_days"
## [7] "lic_beds" "avail_beds"
## [9] "intensive_care_patient_days" "acute_care_patient_days"
## [11] "surgical_services_op_min" "laboratory_cap_units"
## [13] "radiology_rvus" "ct_scanning_hect_units"
## [15] "emergency_room_visits"
Out of all datasets, we construct a consolidated dataset based on selected features. In order to normalize the data (except financial ratios), we use:
\[denominator = caseMixIndex * admissions\]
We use random forest in order to predict and pinpoint factors influencing the following variables:
We chose median absolute percentage error (MdAPE) to compare the effect of outliers on our performance.